基于人工智能的图像分析在生物信息学中的应用

Z. Car, N. Anđelić, I. Lorencin, J. Musulin, D. Štifanić, Sandi Baressi Baressi Šegota
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引用次数: 0

摘要

在今天的临床实践中,图像数据的收集是一个非常常见的程序。许多诊断方法产生这样的数据——计算机断层扫描(CT)、x射线照相、磁共振成像(MRI)等。这个数据收集过程允许使用计算机视觉方法来进行分析和诊断。在包括医学在内的许多领域,基于人工智能(AI)的算法一再被证明是性能最好的计算机视觉算法。基于人工智能——或者更准确地说是基于机器学习(ML)——的算法具有从数据本身学习数据中包含的模式的能力。其中表现最好的算法是人工神经网络(ann),或者更准确地说是卷积神经网络(cnn)。他们的缺点是需要大量的数据——但正如前面提到的,在今天的临床实践中收集的数据量很大,而且还在不断增加。这允许开发智能诊断系统,该系统旨在作为卫生专业人员的支持系统。在本文中,首先给出了该领域的标准实践和回顾-重点是挑战和最佳实践。然后,给出了多个应用人工智能算法分析的研究实例,包括各种类型癌症(膀胱癌和口腔癌)的诊断,以及COVID-19严重程度诊断和图像质量确定。
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APPLICATION OF ARTIFICIAL INTELLIGENCE-BASED IMAGE ANALYSIS IN BIOINFORMATICS
The collection of image data is an extremely common procedure in clinical practice today. Many of the diagnostic approaches generate such data – computed tomography (CT), X-ray radiography, magnetic resonance imaging (MRI), and others. This data collection process allows for the use of computer vision approaches to be applied with the goal of analysis and diagnostics. Artificial Intelligence (AI) based algorithms have repeatedly been shown to be the best performing computer vision algorithms, in many fields including medicine. AI-based – or more precisely machine learning (ML) based, algorithms have capabilities which allow them to learn the patterns contained in the data from the data itself. Among the best performing algorithms are artificial neural networks (ANNs), or more precisely convolutional neural networks (CNNs). Their pitfall is the need for the large amounts of data – but as it has been previously mentioned, the amount of data collected in today’s clinical practice is large and ever increasing. This allows for the development of Smart Diagnostic systems which are meant to serve as support systems to the health professionals. In this paper first, the standard practices and review of the field is given – with the focus on challenges and best practices. Then, multiple examples of the research applying AI-based algorithm analysis are given – including diagnostics of various cancer types (bladder and oral) as well as COVID-19 severity diagnostics and image quality determination.
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